Relational database models have gained popularity in recent decades over more traditional network and hierarchical models. Continuous advances in computational resources and memory leverage the transactional atomicity of large databases for ensuring database consistency across many geographically distributed users. The banking and finance industries have relied on relational databases for financial transactions, in which global consistency ensures that the same DB object (account, for example) is not simultaneously accessed by separate users, for example. This conventional approach relies on 1) adequate database size and 2) a database application operable for performing the required accesses. Both factors increase cost, which has largely been acceptable as storage and computation efficiency increase. However, larger institutions which operate such traditional models find acceptability in maintaining massive volumes of storage for supporting an equally vast user base.
Traditional, rigid, relational models do not lend themselves well to modern database trends where substantial computational power is available to even modest users, due largely to the Internet. Many users seek information, rather than rigid, absolute, results, such as financial or scientific computations. In such a context, it may be less compelling whether your search engine returns the top 499 or 500 documents pertinent to a topical query, in contrast to the traditional model employed in an accounting context for computing payroll or accounts receivable, for example. However, the traditional model, characterized by relational databases executing SQL (Structured Query Language) operations persists because it has been effective, if not inexpensive and efficient, and is embedded in many of the contexts where it is employed.